**XLNet** is an autoregressive [Transformer](https://paperswithcode.com/method/transformer) that leverages the best of both autoregressive language modeling and autoencoding while attempting to avoid their limitations. Instead of using a fixed forward or backward factorization order as in conventional autoregressive models, XLNet maximizes the expected log likelihood of a sequence w.r.t. all possible permutations of the factorization order. Thanks to the permutation operation, the context for each position can consist of tokens from both left and right. In expectation, each position learns to utilize contextual information from all positions, i.e., capturing bidirectional context.

Additionally, inspired by the latest advancements in autogressive language modeling, XLNet integrates the segment recurrence mechanism and relative encoding scheme of [Transformer-XL](https://paperswithcode.com/method/transformer-xl) into pretraining, which empirically improves the performance especially for tasks involving a longer text sequence.

XLNet

XLNet: Generalized Autoregressive Pretraining for Language Understanding

**Blink** is a communication library for inter-GPU parameter exchange that achieves near-optimal link utilization. To handle topology heterogeneity from hardware generations or partial allocations from cluster schedulers, Blink dynamically generates optimal communication primitives for a given topology. Blink probes the set of links available for a given job at runtime and builds a topology with appropriate link capacities. Given the topology, Blink achieves the optimal communication rate by packing spanning trees, that can utilize more links (Lovasz, 1976; Edmonds, 1973) when compared to rings. The authors use a multiplicative-weight update based approximation algorithm to quickly compute the maximal packing and extend the algorithm to further minimize the number of trees generated. Blink’s collectives extend across multiple machines effectively utilizing all available network interfaces.

Blink Communication

Blink: Fast and Generic Collectives for Distributed ML

**AEDA**, or **An Easier Data Augmentation**, is a type of data augmentation technique for text classification which includes only the insertion of various punctuation marks into the input sequence. AEDA preserves all the input information and does not mislead the network since it keeps the word order intact while changing their positions in that the words are shifted to the right.

AEDA

AEDA: An Easier Data Augmentation Technique for Text Classification

**RandomResizedCrop** is a type of image data augmentation where a crop of random size of the original size and a random aspect ratio of the original aspect ratio is made. This crop is finally resized to given size.

Image Credit: [Apache MXNet](https://mxnet.apache.org/versions/1.5.0/tutorials/gluon/data_augmentation.html)

Random Resized Crop

A **Sparse Autoencoder** is a type of autoencoder that employs sparsity to achieve an information bottleneck. Specifically the loss function is constructed so that activations are penalized within a layer. The sparsity constraint can be imposed with [L1 regularization](https://paperswithcode.com/method/l1-regularization) or a KL divergence between expected average neuron activation to an ideal distribution $p$.

Image: [Jeff Jordan](https://www.jeremyjordan.me/autoencoders/). Read his blog post (click) for a detailed summary of autoencoders.

Sparse Autoencoder

**CPT**, or **Chinese Pre-trained Unbalanced Transformer**, is a pre-trained unbalanced [Transformer](https://paperswithcode.com/method/transformer) for Chinese natural language understanding (NLU) and natural language generation (NLG) tasks. CPT consists of three parts: a shared encoder, an understanding decoder, and a generation decoder. Two specific decoders with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model. Two specific decoders with a shared encoder are pre-trained with masked language modeling (MLM) and denoising auto-encoding (DAE) tasks, respectively. With the partially shared architecture and multi-task pre-training, CPT can (1) learn specific knowledge of both NLU or NLG tasks with two decoders and (2) be fine-tuned flexibly that fully exploits the potential of the model.

Chinese Pre-trained Unbalanced Transformer

CPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

This is a general approach to convert a neural network into an analytic equation. The technique works as follows:

1. Encourage sparse latent representations
2. Apply symbolic regression to approximate the transformations between in/latent/out layers
3. Compose the symbolic expressions.

In the [paper](https://arxiv.org/abs/2006.11287), we show that we find the correct known equations, including force laws and Hamiltonians, can be extracted from the neural network. We then apply our method to a non-trivial cosmology example-a detailed dark matter simulation-and discover a new analytic formula which can predict the concentration of dark matter from the mass distribution of nearby cosmic structures. The symbolic expressions extracted from the GNN using our technique also generalized to out-of-distribution data better than the GNN itself. Our approach offers alternative directions for interpreting neural networks and discovering novel physical principles from the representations they learn.

Symbolic Deep Learning

Discovering Symbolic Models from Deep Learning with Inductive Biases

**Online Normalization** is a normalization technique for training deep neural networks. To define Online Normalization. we replace arithmetic averages over the full dataset in with exponentially decaying averages of online samples. The decay factors $\alpha\_{f}$ and $\alpha\_{b}$ for forward and backward passes respectively are hyperparameters for the technique.

We allow incoming samples $x\_{t}$, such as images, to have multiple scalar components and denote
feature-wide mean and variance by $\mu\left(x\_{t}\right)$ and $\sigma^{2}\left(x\_{t}\right)$. The algorithm also applies to outputs of fully connected layers with only one scalar output per feature. In fact, this case simplifies to $\mu\left(x\_{t}\right) = x\_{t}$ and $\sigma\left(x\_{t}\right) = 0$. Denote scalars $\mu\_{t}$ and $\sigma\_{t}$ to denote running estimates of mean and variance across
all samples. The subscript $t$ denotes time steps corresponding to processing new incoming samples.

Online Normalization uses an ongoing process during the forward pass to estimate activation means
and variances. It implements the standard online computation of mean and variance generalized to processing multi-value samples and exponential averaging of sample statistics. The
resulting estimates directly lead to an affine normalization transform.

$$ y\_{t} = \frac{x\_{t} - \mu\_{t-1}}{\sigma\_{t-1}} $$ 

$$ \mu\_{t} = \alpha\_{f}\mu\_{t-1} + \left(1-\alpha\_{f}\right)\mu\left(x\_{t}\right) $$

$$ \sigma^{2}\_{t} = \alpha\_{f}\sigma^{2}\_{t-1} + \left(1-\alpha\_{f}\right)\sigma^{2}\left(x\_{t}\right) + \alpha\_{f}\left(1-\alpha\_{f}\right)\left(\mu\left(x\_{t}\right) - \mu\_{t-1}\right)^{2} $$

Online Normalization

Online Normalization for Training Neural Networks

**Shake-Shake Regularization**  aims to improve the generalization ability of multi-branch networks by replacing the standard summation of parallel branches with a stochastic affine combination. A typical pre-activation [ResNet](https://paperswithcode.com/method/resnet) with 2 residual branches would follow this equation:

$$x\_{i+1} = x\_{i} + \mathcal{F}\left(x\_{i}, \mathcal{W}\_{i}^{\left(1\right)}\right) + \mathcal{F}\left(x\_{i}, \mathcal{W}\_{i}^{\left(2\right)}\right) $$

Shake-shake regularization introduces a random variable $\alpha\_{i}$  following a uniform distribution between 0 and 1 during training:

$$x\_{i+1} = x\_{i} + \alpha\mathcal{F}\left(x\_{i}, \mathcal{W}\_{i}^{\left(1\right)}\right) + \left(1-\alpha\right)\mathcal{F}\left(x\_{i}, \mathcal{W}\_{i}^{\left(2\right)}\right) $$

Following the same logic as for [dropout](https://paperswithcode.com/method/dropout), all $\alpha\_{i}$ are set to the expected value of $0.5$ at test time.

Shake-Shake Regularization

Shake-Shake regularization

Multiscale Attention ViT with Late fusion (MAVL) is a multi-modal network, trained with aligned image-text pairs, capable of performing targeted detection using human understandable natural language text queries. It utilizes multi-scale image features and uses deformable convolutions with late multi-modal fusion. The authors demonstrate excellent ability of MAVL as class-agnostic object detector when queried using general human understandable natural language command, such as "all objects", "all entities", etc.

MAVL

Class-agnostic Object Detection with Multi-modal Transformer

**gCANS**, or **Global Coupled Adaptive Number of Shots**, is a variational quantum algorithm for stochastic gradient descent. It adaptively allocates shots for the measurement of each gradient component at each iteration. The optimizer uses a criterion for allocating shots that incorporates information about the overall scale of the shot cost for the iteration.

gCANS

Adaptive shot allocation for fast convergence in variational quantum algorithms

VisualBERT aims to reuse self-attention to implicitly align elements of the input text and regions in the input image. Visual embeddings are used to model images where the representations are represented by a bounding region in an image obtained from an object detector. These visual embeddings are constructed by summing three embeddings: 1) visual feature representation, 2) a segment embedding indicate whether it is an image embedding, and 3) position embedding. Essentially, image regions and language are combined with a Transformer to allow self-attention to discover implicit alignments between language and vision. VisualBERT is trained using COCO, which consists of images paired with captions. It is pre-trained using two objectives: masked language modeling objective and sentence-image prediction task. It can then be fine-tuned on different downstream tasks.

VisualBERT

VisualBERT: A Simple and Performant Baseline for Vision and Language

**Adversarial-Learned Loss for Domain Adaptation** is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.

ALDA

Adversarial-Learned Loss for Domain Adaptation

**Domain-Symmetric Network**, or **SymmNet**, is an algorithm for unsupervised multi-class domain adaptation. It features an adversarial strategy of domain confusion and discrimination.

SymmNet

Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice

**BTmPG**, or **Back-Translation guided multi-round Paraphrase Generation**, is a multi-round paraphrase generation method that leverages back-translation to guide paraphrase model during training and generates paraphrases in a multiround process. The model regards paraphrase generation as a monolingual translation task. Given a paraphrase pair $\left(S\_{0}, P\right)$, which $S\_{0}$ is the original/source sentence and $P$ is the target paraphrase given in the dataset. In the first round generation, we send $S\_{0}$ into a paraphrase model to generate a paraphrase $S\_{1}$. In the second round generation, we use the $S\_{1}$ as the input of the model to generate a new paraphrase $S\_{2}$. And so forth, in the $i$-th round generation, we send $S\_{i−1}$ into the paraphrase model to generate $S\_{i}$.
.

BTmPG

Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach

Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table understanding. However, existing work generally relies on heavily-engineered task- specific features and model architectures. In this paper, we present TURL, a novel framework that introduces the pre-training/fine- tuning paradigm to relational Web tables. During pre-training, our framework learns deep contextualized representations on relational tables in an unsupervised manner. Its universal model design with pre-trained representations can be applied to a wide range of tasks with minimal task-specific fine-tuning.
Specifically, we propose a structure-aware Transformer encoder to model the row-column structure of relational tables, and present a new Masked Entity Recovery (MER) objective for pre-training to capture the semantics and knowledge in large-scale unlabeled data. We systematically evaluate TURL with a benchmark consisting of 6 different tasks for table understanding (e.g., relation extraction, cell filling). We show that TURL generalizes well to all tasks and substantially outperforms existing methods in almost all instances.

TURL

TURL: Table Understanding through Representation Learning

The **BP-Transformer (BPT)** is a type of [Transformer](https://paperswithcode.com/method/transformer) that is motivated by the need to find a better balance between capability and computational complexity for self-attention. The architecture partitions the input sequence into different multi-scale spans via binary partitioning (BP). It incorporates an inductive bias of attending the context information from fine-grain to coarse-grain as the relative distance increases. The farther the context information is, the coarser its representation is.
BPT can be regard as graph neural network, whose nodes are the multi-scale spans. A token node can attend the smaller-scale span for the closer context and the larger-scale span for the longer distance context. The representations of nodes are updated with [Graph Self-Attention](https://paperswithcode.com/method/graph-self-attention).

BP-Transformer

BP-Transformer: Modelling Long-Range Context via Binary Partitioning

**STAC** is a semi-supervised framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentations. We generate pseudo labels (i.e., bounding boxes and their class labels) for unlabeled data using test-time inference, including NMS , of the teacher model trained with labeled data. We then compute unsupervised loss with respect to pseudo labels whose confidence scores are above a threshold $\tau$ . The strong augmentations are applied for augmentation consistency during the model training. Target boxes are augmented when global geometric transformations are used.

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